Mining fuzzy association rules from questionnaire data

Chen, Yen-Liang; Weng, Cheng-Hsiung

doi:10.1016/j.knosys.2008.06.003

Cited by 82 publications

(33 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fuzzy sets are widely used in decision analysis, approximate reasoning, engineering, pattern recognition and information systems (Klir & Yuan, 1995). It is used to handle values or concepts have unclear semantic boundaries or semantic overlap (Chen & Weng, 2009). …”

Section: A Cmentioning

confidence: 99%

Student rules: Exploring patterns of students’ computer-efficacy and engagement with digital technologies in learning

Howard

Yang

2016

Computers & Education

View full text Add to dashboard Cite

show abstract

Section: A Cmentioning

confidence: 99%

Student rules: Exploring patterns of students’ computer-efficacy and engagement with digital technologies in learning

Howard

Yang

2016

Computers & Education

View full text Add to dashboard Cite

show abstract

“…The application of data mining techniques to the tabular element of questionnaires does not present a particular challenge, tabular data mining is well understood. One example of the use of established data mining techniques directed at the tabular component of questionnaires is reported in [6] where Fuzzy Association Rule Mining (FARM) is used to extract knowledge from the questionnaires. The mining of the free text component of questionnaires is more challenging and requires recourse to text mining techniques.…”

Section: Related Workmentioning

confidence: 99%

An Investigation Concerning the Generation of Text Summarisation Classifiers Using Secondary Data

García-Constantino

Coenen

Noble

et al. 2011

Machine Learning and Data Mining in Pattern Recognition

View full text Add to dashboard Cite

Abstract. An investigation into the potential effectiveness of generating text classifiers from secondary data for the purpose of text summarisation is described. The application scenario assumes a questionnaire corpus where we wish to provide a summary regarding the nature of the free text element of such questionnaires, but no suitable training data is available. The advocated approach is to build the desired text summarisation classifiers using secondary data and then apply these classifiers, for the purpose of text summarisation, to the primary data. We refer to this approach using the acronym CGUSD (Classifier Generation Using Secondary Data). The approach is evaluated using a real questionnaire data obtained as part of the SAVSNET project.

show abstract

“…Data mining method can also apply to explore the data to describe the status of learners (Hu et al, 2014). Association rules is one of the most famous data mining methods, which explored the relationships among the data attributes (Agrawal et al, 1993;Han et al, 2001;Chen & Weng, 2009;Weng, 2011;Sowan et al, 2013;Shabana & Samuel, 2015;Palacios et al, 2015). An association rule mining is to generate all frequent itemsets and association rules that satisfy the minimum support and confidence values from the database.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Rare Factors Exploration of Learning Effectiveness by Using Fuzzy Data Mining

Chen

Chang

2017

EURASIA J MATH SCI T

View full text Add to dashboard Cite

The phenomenon of low fertility has been negative impacted on the social structure of the educational environment in Taiwan. To increase the learning effectiveness of students became the most important issue for the Universities in Taiwan. Due to the subjective judgment of evaluators and the attributes of influenced factors are always fuzzy, it is not easy to measure the learning effectiveness by using the crisp values for the qualitative factors. In additional, the negative behaviors or the infrequent learning data (rare item sets) cannot easily excavate from educational database. Therefore, a systematic mining method is proposed here by combining fuzzy set theory with data mining technology to explore the key factors of learning effectiveness from the infrequent data. A case study implemented in this paper and showed that the proposed method can effectively find the important factors and valuable patterns from the survey data. The results of this paper can provide the important information for teachers to help students increase the learning effectiveness. Finally, the conclusions are addressed at the end of this paper.

show abstract

Mining fuzzy association rules from questionnaire data

Cited by 82 publications

References 23 publications

Student rules: Exploring patterns of students’ computer-efficacy and engagement with digital technologies in learning

Student rules: Exploring patterns of students’ computer-efficacy and engagement with digital technologies in learning

An Investigation Concerning the Generation of Text Summarisation Classifiers Using Secondary Data

A Study on the Rare Factors Exploration of Learning Effectiveness by Using Fuzzy Data Mining

Contact Info

Product

Resources

About